Ori Bar-Ilan, Roni Stern and Meir Kalech
The Twenty Seventh International Workshop on Principles of Diagnosis (DX-17), 2017
Software diagnosis algorithms aim to identify the faultysoftware components that caused a failure. A key challengesof existing software diagnosis algorithms is how to prioritizethe outputted diagnoses. To do so, previous work proposeda method for estimating the likelihood that each diagnosisis correct. Computing these diagnosis likelihoods is nontrivial.We propose to do this by learning a behavior modelof the software components and use it to identify abnormallybehaving components. In this work we show the potentialof such an approach by performing an empirical evaluationon a synthetic behavior model of the components. The resultsshow that even an imperfect behavior model is usefulin improving diagnosis accuracy and minimizing wastedtroubleshooting efforts.